Differential privacy [1]} has become a de facto standard for releasing data in a privacy-preserving way. It has been increasingly adopted in industry [2]}, [3]}, [4]}, [5]}, [6]} and the public sector [7]}, [8]}, [9]}, [10]}.
Crucial to any differentially private system is a set of privacy mechanisms, the building blocks of larger privacy-preserving algorithms. Privacy mechanisms inject randomness into non-private computations in order to ensure privacy protections. However, developing such mechanisms is a daunting task for several reasons.
